Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer

The emerging field of precision oncology relies on the accurate pinpointing of alterations in the molecular profile of a tumor to provide personalized targeted treatments. Current methodologies in the field commonly include the application of next generation sequencing technologies to a tumor sample, followed by the identification of mutations in the DNA known as somatic variants. The differentiation of these variants from sequencing error poses a classic classification problem, which has traditionally been approached with Bayesian statistics, and more recently with supervised machine learning methods such as neural networks. Although these methods provide greater accuracy, classic neural networks lack the ability to indicate the confidence of a variant call. In this paper, we explore the performance of deep Bayesian neural networks on next generation sequencing data, and their ability to give probability estimates for somatic variant calls. In addition to demonstrating similar performance in comparison to standard neural networks, we show that the resultant output probabilities make these better suited to the disparate and highly-variable sequencing data-sets these models are likely to encounter in the real world. We aim to deliver algorithms to oncologists for which model certainty better reflects accuracy, for improved clinical application. By moving away from point estimates to reliable confidence intervals, we expect the resultant clinical and treatment decisions to be more robust and more informed by the underlying reality of the tumor molecular profile.

READ FULL TEXT

page 4

page 5

research
12/04/2019

Safety and Robustness in Decision Making: Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer

The genomic profile underlying an individual tumor can be highly informa...
research
11/26/2018

Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection

DNA sequencing to identify genetic variants is becoming increasingly val...
research
07/02/2018

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate su...
research
07/02/2018

Personalized Radiotherapy Planning for Glioma Using Multimodal Bayesian Model Calibration

Existing radiotherapy (RT) plans for brain tumors derive from population...
research
07/02/2018

Personalized Radiotherapy Design for Glioblastoma Using Mathematical Models, Multimodal Scans and Bayesian Inference

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate su...
research
11/28/2022

Graph Neural Networks for Breast Cancer Data Integration

International initiatives such as METABRIC (Molecular Taxonomy of Breast...
research
02/24/2022

Bayesian Deep Learning for Graphs

The adaptive processing of structured data is a long-standing research t...

Please sign up or login with your details

Forgot password? Click here to reset